arXiv subjects

Adithya Sireesh

Publications and source records attributed to Adithya Sireesh.

5 recordsLinked to original sources

arXiv2026

Quantum Elastic Network Models and their Application to Graphene

Molecular dynamics simulations are a central computational methodology in materials design for relating atomic composition to mechanical properties. However, simulating materials with atomic-level resolution on a macroscopic scale is infeasible on current classical hardware, even when using the simplest elastic network models (ENMs) that represent molecular vibrations as a network of coupled oscillators. To address this issue, we introduce Quantum Elastic Network Models (QENMs) and utilize the quantum algorithm of Babbush et al. (PRX, 2023), which offers an exponential advantage when simulating systems of coupled oscillators. Here, we extend their algorithm in 2D systems and demonstrate how our method enables the efficient simulation of planar materials. As an example, we apply our algorithm to the task of simulating a 2D graphene sheet. We analyze the complexity for initial-state preparation, Hamiltonian simulation, and measurement of this material, and provide two real-world applications: heat transfer and the out-of-plane rippling effect. We estimate that an atomistic simulation of a graphene sheet on the centimeter scale, classically requiring hundreds of petabytes of memory and prohibitive runtimes, could be encoded and simulated with as few as $\sim 160$ logical qubits.

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arXiv2025

Logical accreditation: a framework for efficient certification of fault-tolerant computations

As fault-tolerant quantum computers scale, certifying the accuracy of computations performed with encoded logical qubits will soon become classically intractable. This creates a critical need for scalable, device-independent certification methods. In this work, we introduce logical accreditation, a framework for efficiently certifying the correctness of quantum computations performed on logical qubits. Our protocol is robust against general noise models, far beyond those typically considered in performance analyses of quantum error-correcting codes. Through numerical simulations, we demonstrate that logical accreditation can scalably certify quantum advantage experiments and indicate the crossover point where encoded computations begin to outperform physical computations. The framework also enables evaluation of whether logical error rates are sufficiently low that error mitigation can be efficiently performed, extends entropy benchmarking to the regime of fault-tolerant computation, and upper bounds the infidelity of the logical output state of a computation. Underlying the framework is a novel randomised compilation scheme that converts arbitrary logical circuit noise into stochastic Pauli noise. This scheme includes a method for twirling non-transversal logical gates beyond the standard $T$ gate, resolving an open problem posed by [Piveteau et al. PRL 127, 200505 (2021)]. By bridging fault-tolerant computation and computational certification, logical accreditation offers a scalable, practical means of certifying the accuracy of quantum computations performed using encoded logical qubits.

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arXiv2025

Disentangling quantum autoencoder

Entangled quantum states are highly sensitive to noise, which makes it difficult to transfer them over noisy quantum channels or to store them in quantum memory. Here, we propose the disentangling quantum autoencoder (DQAE) to encode entangled states into single-qubit product states. The DQAE provides an exponential improvement in the number of copies needed to transport entangled states across qubit-loss or leakage channels compared to unencoded states. The DQAE can be trained in an unsupervised manner from entangled quantum data. For general states, we train via variational quantum algorithms based on gradient descent with purity-based cost functions, while stabilizer states can be trained via a Metropolis algorithm. For particular classes of states, the number of training data needed to generalize is surprisingly low: For stabilizer states, DQAE generalizes by learning from a number of training data that scales linearly with the number of qubits, while only $1$ training sample is sufficient for states evolved with the transverse-field Ising Hamiltonian. Our work provides practical applications for enhancing near-term quantum computers.

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arXiv2025

Optimized circuits for windowed modular arithmetic with applications to quantum attacks against RSA

Windowed arithmetic [Gidney, 2019] is a technique for reducing the cost of quantum arithmetic circuits with space--time tradeoffs using memory queries to precomputed tables. It can reduce the asymptotic cost of modular exponentiation from $O\left(n^3\right)$ to $O\left(n^3/\log^2 n\right)$ operations, resulting in the current state-of-the-art compilations of quantum attacks against modern cryptography. In this work we introduce four optimizations to windowed modular exponentiation. We (1) show how the cost of unlookups can be reduced by $66\%$ asymptotically in the number of bits, (2) illustrate how certain addresses can be bypassed, reducing both circuit depth and the overall lookup cost, (3) demonstrate that multiple lookup--addition operations can be merged into a single, larger lookup at the start of the modular exponentiation circuit, and (4) reduce the depth of the unary conversion for unlookups. On a logical level, this leads to a $3\%$ improvement in Toffoli count and Toffoli depth for modular exponentiation circuits relevant to cryptographic applications. This translates to some improvements on [Gidney and Eker\r{a}, 2021]'s factoring algorithm: for a given number of physical qubits, our improvements show a reduction in the expected runtime from $2\%$ to $6\%$ for factoring $\mathsf{RSA}$-$2048$ integers.

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arXiv2024

Measurement-based uncomputation of quantum circuits for modular arithmetic

Measurement-based uncomputation (MBU) is a technique used to perform probabilistic uncomputation of quantum circuits. We formalize this technique for the case of single-qubit registers, and we show applications to modular arithmetic. First, we present formal statements for several variations of quantum circuits performing non-modular addition: controlled addition, addition by a constant, and controlled addition by a constant. We do the same for subtraction and comparison circuits. This addresses gaps in the current literature, where some of these variants were previously unexplored. Then, we shift our attention to modular arithmetic, where again we present formal statements for modular addition, controlled modular addition, modular addition by a constant, and controlled modular addition by a constant, using different kinds of plain adders and combinations thereof. We introduce and prove a "MBU lemma" in the context of single-qubit registers, which we apply to all aforementioned modular arithmetic circuits. Using MBU, we reduce the Toffoli count and depth by $10\%$ to $15\%$ for modular adders based on the architecture of [VBE96], and by almost $25\%$ for modular adders based on the architecture of [Bea02]. Our results have the potential to improve other circuits for modular arithmetic, such as modular multiplication and modular exponentiation, and can find applications in quantum cryptanalysis.

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